class_docstr: Tests correctness of the power transformation.
code: def testDtype(self):
      bijector = power_transform.PowerTransform(
          power=0.2, validate_args=True
      )
      x = self.make_input([-0.5, 1.0, 3.0])
      y = self.make_input([0.3, 3.0, 1.2])
      self.assertIs(bijector.forward(x).dtype, x.dtype)
      self.assertIs(bijector.inverse(y).dtype, y.dtype)
      self.assertIs(
          bijector.forward_log_det_jacobian(x, event_ndims=0).dtype,
          x.dtype,
      )
      self.assertIs(
          bijector.inverse_log_det_jacobian(y, event_ndims=0).dtype,
          y.dtype,
      )
  
file_path: probability/tensorflow_probability/python/bijectors/power_transform_test.py
imports: import numpy
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import power_transform
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 4 for calling the PowerTransformBijectorTest obj's underlying member method code with example usage: obj.testDtype() without return types